Search Results (549)

This study examines the impact of technological capabilities across industries on venture capital (VC) inflows. Technological capabilities were proxied by industry-level patent applications and R&D expenditures. VC inflows were derived from annual investment statistics published by the Ministry of SMEs and Startups and the Korea Venture Capital Association. Multiple regression analysis shows that industries with more patent applications are more likely to attract venture investments. Moreover, the relationships among patents, R&D, and venture inflows vary significantly across industries. In the biomedical industry, VC inflows show strong positive correlations with patent applications (r = 0.762, p < 0.001) and R&D investment (r = 0.900, p < 0.001). In contrast, in the information and communication technology manufacturing sector, the association between patent applications and VC inflows is not statistically significant (R² = 0.002, p > 0.05), implying that the conversion efficiency of technological outputs into investment differs according to the industrial structure. This study provides evidence of how technological development translates into commercialization and private investment. The findings contribute to a nuanced understanding of success factors in technology-based startups by industry and may serve as a foundation for the formulation of effective policy measures and investment strategies to promote private capital inflows. Full article

Metamaterials are artificial materials composed of special microstructures that have properties with unusual and useful features and can be applied to many fields. With their unique properties and sensitivity to external stimuli, metamaterials offer design flexibility to users. Traditional manufacturing is often not up to the task of creating metamaterials, which are now more accurately and more effectively analyzed than they were in the past. Recent advances in additive manufacturing (AM) have achieved remarkable success, with ensemble machine learning models demonstrating R² values exceeding 0.97 and accuracy improvements of 9.6% over individual approaches. State-of-the-art multiphoton polymerization (MPP) techniques now reach submicron resolution (<1 μm), while selective laser melting (SLM) processes provide 20–100 μm precision for metallic metamaterials. This work offers a comprehensive review of additively manufactured 3D metamaterials, focusing on three categories of their fabrication: electromagnetic (achieving bandgaps up to 470 GHz), acoustic (providing 90% sound suppression at targeted frequencies), and mechanical (demonstrating Poisson’s ratios from −0.8 to +0.8). The relationship between different types of AM processes used in creating 3D objects and the properties of the resulting materials has been systematically reviewed. This research aims to address gaps and develop new applications to meet the modern demand for the broader use of metamaterials in advanced devices and systems that require high efficiency for sophisticated, high-performance applications. Full article

In modern pharmaceutical research and development (R&D), drug discovery remains a challenging process. Artificial intelligence (AI) has been extensively incorporated into various phases of drug discovery and development. AI enable effectively extract molecular structural features, perform in-depth analysis of drug–target interactions, and systematically model the relationships among drugs, targets, and diseases. These approaches improve prediction accuracy, accelerate discovery timelines, reduce costs from trial and error methods, and enhance success probabilities. This review summarizes recent advances in AI applications for drug design, including target identification, synthetic accessibility prediction, lead optimization, and ADMET property evaluation. Furthermore, it introduces various deep learning tools to guide researchers in selecting and implementing the most appropriate AI-driven strategies throughout the drug discovery process. We hope it can establish a conceptual framework intended to advance AI-driven methodologies in pharmaceutical research by comprehensively organizing novel perspectives and critical insights. Full article

Groundwater plays a critical role in maintaining streamflow during low-flow periods. However, accurately quantifying groundwater flow still remains a modeling challenge. Prolonged low-flow or drought conditions necessitate long-term simulations, further increasing the complexity of achieving reliable results. To address these issues, a novel modeling framework (HYD module in LoFloDes) that integrates a one-dimensional (1D) river module with two-dimensional (2D) groundwater module via bidirectional coupling, enabling robust and accurate simulations of both groundwater and river dynamics throughout their interactions, especially over extended periods, was developed. The HYD module was applied to the Rur River, calibrated using gridded groundwater data, groundwater and river gauge data from 2002 to 2005 and validated from 1991 to 2020. During validation periods, the simulated river and groundwater levels generally reproduced observed trends, although suboptimal performance at certain gauges is attributed to unmodeled local anthropogenic influences. Comparative simulations demonstrated that the incorporation of groundwater–river interactions markedly enhanced model performance, especially at the downstream Stah gauge, where the coefficient of determination (R²) increased from 0.83 without interaction to 0.9 with interaction. Consistent with spatio-temporal patterns of this interaction, simulated groundwater contributions increased from upstream to downstream and were elevated during low-flow months. These findings underscore the important role of groundwater contributions in local river dynamics along the Rur River reach. The successful application of the HYD module demonstrates its capacity for long-term simulations of coupled groundwater–surface water systems and underscores its potential as a valuable tool for integrated river and groundwater resources management. Full article

Background: Cryopreservation of hematopoietic stem cells (HSCs) at −80 °C using uncontrolled-rate freezing is frequently employed in resource-constrained settings, yet concerns remain regarding long-term viability and clinical efficacy. Reliable post-thaw assessment is essential to ensure graft quality and engraftment success. Methods: This single-center, retrospective study evaluated 72 cryopreserved stem cell products from 25 patients stored at −80 °C for a median of 868 days. Viability was assessed using both acridine orange (AO) staining and 7-AAD (7-aminoactinomycin D) flow cytometry at three time points: collection (T0), pre-infusion (T1), and delayed post-thaw evaluation (T2). Associations between viability loss, storage duration, and clinical engraftment outcomes were analyzed. Results: Median post-thaw viability remained high (94.8%) despite a moderate time-dependent decline (~1.02% per 100 days; R² = 0.283, p < 0.001). Mean viability loss at T2 was 9.2% (AO) and 6.6% (flow cytometry). AO demonstrated greater sensitivity to delayed degradation, with a significant difference between methods (p < 0.001). Engraftment kinetics were preserved in most patients, with neutrophil and platelet recovery primarily influenced by disease type rather than product integrity. Notably, storage duration and donor age were not significantly associated with engraftment outcomes or CD34⁺ cell dose. Conclusions: Long-term cryopreservation at −80 °C maintains HSC viability sufficient for durable engraftment, despite gradual decline. While transplant outcomes are primarily dictated by disease biology and remission status, AO staining provides enhanced sensitivity for detecting delayed cellular damage. Notably, our viability-loss model offers a practical framework for predicting product quality, potentially supporting graft selection and clinical decision-making in real-world, resource-constrained transplant settings. Full article

The growing need for safe, cheap and sustainable earthquake-resistant buildings means that efficient methods for optimal seismic design must be found. The complexity and nonlinearity of the problem can be addressed using advanced automated techniques. This paper presents an intelligent three-step procedure for optimally designing earthquake-resistant buildings based on the training (1st step) and successive inversion (2nd step) of Multi-Layer Perceptron Neural Networks. This involves solving the inverse problem of determining the optimal design parameters that meet pre-assigned, code-based performance targets, by means of a gradient-based optimization algorithm (3rd step). The effectiveness of the procedure was tested using an archetypal multistory, moment-resisting, concentrically braced steel frame with active tension diagonal bracing. The input dataset was obtained by varying four design parameters. The output dataset resulted from performance variables obtained through non-linear dynamic analyses carried out under three earthquakes consistent with the Chilean code spectrum, for all cases considered. Three spectrum-consistent records are sufficient for code-based seismic design, while each seismic excitation provides a wealth of information about the behavior of the structure, highlighting potential issues. For optimization purposes, only information relevant to critical sections was used as a performance indicator. Thus, the dataset for training consisted of pairs of design parameter sets and their corresponding performance indicator sets. A dedicated MLP was trained for each of the outputs over the entire dataset, which greatly reduced the total complexity of the problem without compromising the effectiveness of the solution. Due to the comparatively low number of cases considered, the leave-one-out method was adopted, which made the validation process more rigorous than usual since each case acted once as a validation set. The trained network was then inverted to find the input design search domain, where a cost-effective gradient-based algorithm determined the optimal design parameters. The feasibility of the solution was tested through numerical analyses, which proved the effectiveness of the proposed artificial intelligence-aided optimal seismic design procedure. Although the proposed methodology was tested on an archetypal building, the significance of the results highlights the effectiveness of the three-step procedure in solving complex optimization problems. This paves the way for its use in the design optimization of different kinds of earthquake-resistant buildings. Full article

Background/Objectives: Spheroid cultures in Matrigel are routinely used to study cell behaviour in complex 3D settings, thereby generating preclinical models of disease. Ideally, researchers would like to modulate gene expression ‘in situ’ for testing novel gene therapies while conserving the spheroid architecture. Here, we aim to provide an efficient method to transfect small RNAs (such as microRNAs and small interfering RNAs, i.e., siRNAs) into human induced pluripotent stem cell (iPSC)-derived 3D lung spheroids, specifically alveolar type II epithelial cells (iAT2) and basal cell (iBC) spheroids. Methods: Transfection of iAT2 spheroids within 3D Matrigel ‘in situ’, whole spheroids released from Matrigel or spheroids dissociated to single cells was explored via flow cytometry using a fluorescently labelled siRNA. Validation of the transfection method was performed in iAT2 and iBC spheroids using siRNA and miRNA mimics and measurement of specific target expression post-transfection. Results: Maximal delivery of siRNA was achieved in serum-free conditions in whole spheroids released from the Matrigel, followed by whole spheroids ‘in situ’. ‘In situ’ transfection of SFTPC-siRNA led to a 50% reduction in the SFTPC mRNA levels in iAT2 spheroids. Transfection of miR-29c mimic and miR-21 pre-miR into iAT2 and iBC spheroids, respectively, led to significant miRNA overexpression, together with a significant decrease in protein levels of the miR-29 target FOXO3a. Conclusions: This study demonstrates successful transfection of iPSC-derived lung spheroids without disruption of their 3D structure using a simple and feasible approach. Further development of these methods will facilitate functional studies in iPSC-derived spheroids utilizing small RNAs. Full article

Background: Hearing and cognition decline with age. Hearing is now considered an independent risk factor for later cognitive impairment. Computerized cognitive auditory training is being discussed as a possible adjunctive therapy approach. Objectives: The aim of this exploratory study is to investigate how the success of a computer-based cognitive auditory training (CCAT) can be measured. For this purpose, the influence of a CCAT on different dimensions of hearing and cognition was determined. Materials and Methods: 23 subjects between 52 and 77 years old were recruited with normacusis to moderate hearing loss. They underwent 40 digital training lessons at home. Before, during, and after completion, concentration ability with the d2-R, memory (VLMT), subjective hearing impairment (HHI), hearing quality (SSQ12), listening effort in noise (ACALES), and speech understanding in noise (GÖSA) were measured. Results and Discussion: In this uncontrolled, non-randomized study, one of the main findings was that cognitive dimensions, namely processing speed, improved by 12.11 ± 16.40 points (p = 0.006), and concentration performance improved by 12.56 ± 13.50 points (p = 0.001), which were not directly trained in CCAT. Learning performance also improved slightly by 4.00 ± 7.00 (p = 0.019). Subjective hearing handicap significantly reduced by 10.70 ± 12.38 (p = 0.001). There were no significant changes in the SSQ-12 (p = 0.979). Hearing effort improved by 1.79 ± 2.13 dB SPL (p = 0.001), 1.75 ± 2.09 (p = 0.001), and 3.32 ± 3.27 dB (p < 0.001), respectively. Speech understanding in noise did not improve significantly. CCAT is likely to improve several dimensions of hearing and cognition. Controlled future studies are needed to investigate its efficacy. Full article

Genome-wide association studies (GWAS) have been a successful tool for identifying quantitative trait loci (QTL) for economically important traits in dairy cows. However, the availability of QTLs linked to phenotypic traits is limited in the literature. In this study, we used GWAS, haplotype association, and fine-mapping analyses to identify candidate variants associated with milk production, health, and reproductive traits in 380 Chinese Holstein cattle from Southern China using whole-genome sequence data. GWAS identified 91 genome-wide significant signals that were annotated to 63 genes associated with milk production, health, and reproductive traits in dairy cattle. Haplotype association analysis further revealed that eight GWAS signals within three QTLs were associated with milk production and health traits of cows. Fine-mapping analysis revealed that 3 GWAS signals (6_92530313_G_A, 10_17185230_G_A, and 10_17209112_T_G) were the potential causal variants. Several candidate genes, including ANKS1B, IL17RD, CNOT6L, AOC1, and TLE3, have been confirmed to be associated with milk production, health, and reproductive traits in dairy cows. These findings significantly contribute to unraveling the genetic basis of economically important traits in Holstein cattle. Full article

Background: Spinal deformity correction surgery, particularly in scoliosis, often necessitates long fusion constructs and complex osteotomies that create significant structural bone defects. These defects threaten the integrity of spinal fusion, potentially compromising surgical outcomes. Bone grafting remains the cornerstone of addressing these defects, traditionally relying on autologous bone. However, limitations such as donor site morbidity and insufficient graft volume have made urgent the development and adoption of biologic substitutes and synthetic alternatives. Additionally, innovations in three-dimensional (3D) printing offer emerging solutions for graft customization and improved osseointegration. Objective: This scoping review maps the evidence of the effectiveness of the use of biologic and synthetic bone grafts in scoliosis surgery. It focusses on the role of novel technologies, particularly osteobiologics in combination with 3D-printed scaffolds, in enhancing graft performance and surgical outcomes. Methods: A comprehensive literature search was conducted using PubMed, Scopus, and the Cochrane Library to identify studies published within the last 15 years. Inclusion criteria focused on clinical and preclinical research involving biologic grafts (e.g., allografts, demineralized bone matrix-DBM, bone morphogenetic proteins-BMPs), synthetic substitutes (e.g., ceramics, polymers), and 3D-printed grafts in the context of scoliosis surgery. Data were extracted on graft type, clinical application, outcome measures, and complications. The review followed PRISMA-ScR guidelines and employed the Arksey and O’Malley methodological framework. Results: The included studies revealed diverse grafting strategies across pediatric and adult populations, with varying degrees of fusion success, incorporation rates, and complication profiles. It also included some anime studies. Emerging 3D technologies demonstrated promising preliminary results but require further validation. Conclusions: Osteobiologic and synthetic bone grafts, including those enhanced with 3D technologies, represent a growing area of interest in scoliosis surgery. Despite promising outcomes, more high-quality comparative clinical studies are needed to guide clinical decision-making and standardize practice. Full article

This paper examines the internationalization dynamics of the Turkish HVAC industry in Germany through a qualitative design based on 24 semi-structured interviews with senior executives. The analysis demonstrates that conformity with EU and German standards, product quality, and continuous innovation are decisive drivers of international expansion. At the same time, economic volatility and regulatory complexity constitute major constraints. Organizational capabilities—particularly internationally experienced managers, R&D capacity, and strategic partnerships—are shown to enhance firms’ competitiveness. Furthermore, diaspora networks provide relational capital that facilitates trust and market embeddedness. The study contributes to international business literature by identifying critical success factors for Turkish HVAC firms in a highly competitive European context. Full article

Background/Objectives: This study investigates the efficacy of working memory training (WMT) using the dual N-back (DNB) task on cognitive performance in young adults with Attention-Deficit/Hyperactivity Disorder (ADHD). Methods: Over the course of at least 18 daily sessions conducted within one month, 106 participants (33 non-medicated ADHD, 42 medicated ADHD, and 45 controls) were randomly assigned to either a fixed dual 1-back (FD1B) training condition or an adaptive DNB condition, wherein the N-back level increased following successful completion of each trial block. Cognitive performance was assessed pre- and post-intervention using the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) Working Memory Index (WMI) and the Corsi Block-Tapping Task. Results: A mixed-design ANOVA revealed significant improvements in DNB performance across all groups, with the adaptive training condition producing larger gains (e.g., a 204.6% improvement in controls, Cohen’s $d=1.85$). WAIS-IV WMI scores—particularly the Digit Span Backward subtest—also improved significantly post-training, with greater effect sizes in the adaptive condition ($d=0.46$) than in FD1B ($d=0.27$). Corsi performance showed very modest gains, showing a surprising tendency to be more associated with the FD1B condition than the adaptive condition. Control participants outperformed the medicated ADHD group on WAIS-IV subtests, although no significant differences emerged between medicated and non-medicated ADHD participants. Correlational analyses indicated task-specific training effects, with adaptive training enhancing associations between DNB and Corsi performance in both controls ($r=0.60$) and medicated ADHD participants ($r=0.51$). Conclusions: This study demonstrates that dual N-back training improves verbal working memory in young adults with ADHD, specifically in a sample without psychiatric comorbidities. Transfer benefit to visuospatial domains appears limited and may not generalize to adolescents, older adults, or individuals with complex clinical profiles. The results underscore the importance of tailoring training protocols to maximize cognitive outcomes across different domains. Full article

In this paper, we develop the explicit finite difference method (FDM) to solve an ill-posed Cauchy problem for the 3D acoustic wave equation in a time domain with the data on a part of the boundary given (continuation problem) in a cube. FDM is one of the numerical methods used to compute the solutions of hyperbolic partial differential equations (PDEs) by discretizing the given domain into a finite number of regions and a consequent reduction in given PDEs into a system of linear algebraic equations (SLAE). We present a theory, and through Matlab Version: 9.14.0.2286388 (R2023a), we find an efficient solution of a dense system of equations by implementing the numerical solution of this approach using several iterative techniques. We extend the formulation of the Jacobi, Gauss–Seidel, and successive over-relaxation (SOR) iterative methods in solving the linear system for computational efficiency and for the properties of the convergence of the proposed method. Numerical experiments are conducted, and we compare the analytical solution and numerical solution for different time phenomena. Full article

As the industrial automation landscape advances, the integration of sophisticated perception and manipulation technologies into robotic systems has become crucial for enhancing operational efficiency and precision. This paper presents a significant enhancement to a robotic system by incorporating the Mask R-CNN deep learning algorithm and the Intel^® RealSense™ D435 camera with the UFactory xArm 5 robotic arm. The Mask R-CNN algorithm, known for its powerful object detection and segmentation capabilities, combined with the depth-sensing features of the D435, enables the robotic system to perform complex tasks with high accuracy. This integration facilitates the detection, manipulation, and precise placement of single objects, achieving 98% detection accuracy, 98% gripping accuracy, and 100% transport accuracy, resulting in a peak manipulation accuracy of 99%. Experimental evaluations demonstrate a 20% improvement in manipulation success rates with the incorporation of depth data, reflecting significant enhancements in operational flexibility and efficiency. Additionally, the system was evaluated under adversarial conditions where structured noise was introduced to test its stability, leading to only a minor reduction in performance. Furthermore, this study delves into cybersecurity concerns pertinent to robotic systems, addressing vulnerabilities such as physical attacks, network breaches, and operating system exploits. The study also addresses specific threats, including sabotage and service disruptions, and emphasizes the importance of implementing comprehensive cybersecurity measures to protect advanced robotic systems in manufacturing environments. To ensure truly robust, secure, and reliable robotic operations in industrial environments, this paper highlights the critical role of international cybersecurity standards and safety standards for the physical protection of industrial robot applications and their human operators. Full article

Background/Objectives: Depression is a mental disorder that can lead to self-harm or suicidal thoughts if left untreated. Psychiatrists often face challenges in identifying the most effective courses of treatment for patients with depression. Two widely recommended depression-related therapies are selective serotonin reuptake inhibitors (SSRIs) and repetitive transcranial magnetic stimulation (rTMS). However, their response rates are approximately 50%, which is relatively low. This study introduces a computer-aided decision (CAD) system designed to determine the effectiveness of depression therapies and recommends the most appropriate treatments for patients. Methods: Each channel of the EEG is plotted in two-dimensional (2D) space via a novel technique called the amplitude polar map (APM). In each channel, the 2D plot of APM is utilized to extract distinctive features via the binary pattern of five successive lines method. The extracted features from each channel are fused to generalize the pattern of EEG signals. The most relevant features are selected via the neighborhood component analysis algorithm. The chosen features are input into a simple feed-forward neural network architecture to classify the EEG signal of a depressed patient into either a respondent to depression therapies or not. The 10-fold cross-validation strategy is employed to ensure unbiased results. Results: The results of our proposed CAD system show accuracy rates of 98.06% and 97.19% for predicting the outcomes of SSRI and rTMS therapies, respectively. In SSRI predictions, prefrontal and parietal channels such as F7, Fz, Fp2, P4, and Pz were the most informative, reflecting brain regions involved in emotional regulation and executive function. In contrast, rTMS prediction relied more on frontal, temporal, and occipital channels such as F4, O2, T5, T3, Cz, and T6, indicating broader network modulation via neuromodulation. Conclusions: The proposed CAD framework holds considerable promise as a clinical decision-support tool, assisting mental health professionals in identifying the most suitable therapeutic interventions for individuals with depression. Full article